Method and system for computing pre-equalizer coefficients
View Patent ↗According to an embodiment of present invention, an algorithm for computing static pre-equalizer coefficients, comprises the steps of determining a length of algorithm iterations; calculating a feedforward coefficient vector associated with a feedforward equalizer; calculating a pre-equalizer coefficient vector associated with a pre-equalizer filter; and performing the steps of calculating for the length of the algorithm iterations; wherein a mean square of an error between an output sequence and a transmitted digital input sequence is minimized.
1. An algorithm for computing static pre-equalizer coefficients, the algorithm comprising the steps of:
determining a length of algorithm iterations;
calculating a feedforward coefficient vector associated with a feedforward equalizer;
calculating a pre-equalizer coefficient vector associated with a pre-equalizer filter; and
performing the steps of calculating for the length of the algorithm iterations; wherein a mean square of an error between an output sequence and a transmitted digital input sequence is minimized.
2. The algorithm of claim 1 , wherein the feedforward equalizer is adaptive.
3. The algorithm of claim 1 , wherein the pre-equalizer filter is non-adaptive.
4. The algorithm of claim 1 , wherein the pre-equalizer coefficient vector comprises even-indexed pre-equalizer filter coefficients {p e,l , lε{0, . . . , N P −1}} where
p e,l =p 2l lε{ 0, . . . , N P −1}
where N P represents half a pre-equalizer length.
5. The algorithm of claim 4 , wherein the pre-equalizer coefficient vector comprises odd-indexed pre-equalizer filter coefficients {p o,l , lε{0, . . . , N P −1}} where
p o,l =p 2l+1 lε{ 0, . . . , N P −1}.
6. The algorithm of claim 1 , wherein the feedforward coefficient vector comprises even feedforward filter coefficients in the i th test case, where
f e,k (i) =f 2k (i) kε{ 0, . . . , N F −1}
where N F represents half a feedforward equalizer length.
7. The algorithm of claim 6 , wherein the feedforward coefficient vector comprises odd feedforward filter coefficients in the i th test case, where
f o,k (i) =f 2k+1 (i) kε{ 0, . . . , N F −1}.
8. The algorithm of claim 1 , wherein the step of calculating a feedforward coefficient vector further comprises calculating a convolution matrix for a noise shaping filter.
9. The algorithm of claim 1 , wherein the pre-equalizer filter increases an effective equalizer length without increasing a number of taps to be trained.
10. The algorithm of claim 1 , wherein the pre-equalizer filter receives an input sequence comprising a combination of a noise sequence from a noise shaping filter and an output of a channel.
11. The algorithm of claim 10 , wherein the channel models a linear distortion.
12. The algorithm of claim 10 , wherein the channel models a linear time invariant filter.
13. The algorithm of claim 1 , wherein the input sequence is received at an upsampling block for generating an upsampled signal.
14. The algorithm of claim 13 , wherein the upsampled signal is received by a channel for generating a channel output and wherein the channel output is combined with a noise sequence for being received by the pre-equalizer filter.
15. The algorithm of claim 1 , wherein the output sequence is produced by a feedback equalizer.
16. The algorithm of claim 15 , wherein the feedback equalizer filter is adaptive.
17. The algorithm of claim 15 , wherein the feedback equalizer filter processes at least one previous decision generated by a decision unit.
18. The algorithm of claim 15 , wherein the output sequence is generated in part by a downsampled output of the feedforward equalizer.